用流匹配技术实现快速高保真无线地图生成
RadioFlow: Efficient Radio Map Construction Framework with Flow Matching
- 基于流匹配，单步采样替代迭代去噪
- 参数量减少8倍，推理速度提升4倍以上
- 适合6G实时电磁数字孪生系统部署
精确且实时的无线地图（RM）生成对下一代无线系统至关重要，但基于扩散模型的方法通常存在模型过大、迭代去噪缓慢、推理延迟高等问题，制约了实际应用。为此，我们提出 extbf{RadioFlow}，一种基于流匹配的生成框架，通过连续传输轨迹学习实现高保真RM生成，支持单步高效采样。与传统扩散模型不同，RadioFlow在训练和推理阶段均显著加速，同时保持重建精度。全面实验表明，相较于领先的扩散基基线（RadioDiff），RadioFlow在性能上达到当前最优，参数量减少最多达8倍，推理速度提升超过4倍。该进展为未来6G网络中可扩展、低功耗、实时的电磁数字孪生提供了可行路径。代码已开源于GitHub。
原文摘要 · Abstract (English)
Accurate and real-time radio map (RM) generation is crucial for next-generation wireless systems, yet diffusion-based approaches often suffer from large model sizes, slow iterative denoising, and high inference latency, which hinder practical deployment. To overcome these limitations, we propose \textbf{RadioFlow}, a novel flow-matching-based generative framework that achieves high-fidelity RM generation through single-step efficient sampling. Unlike conventional diffusion models, RadioFlow learns continuous transport trajectories between noise and data, enabling both training and inference to be significantly accelerated while preserving reconstruction accuracy. Comprehensive experiments demonstrate that RadioFlow achieves state-of-the-art performance with \textbf{up to 8$\times$ fewer parameters} and \textbf{over 4$\times$ faster inference} compared to the leading diffusion-based baseline (RadioDiff). This advancement provides a promising pathway toward scalable, energy-efficient, and real-time electromagnetic digital twins for future 6G networks. We release the code at \href{https://github.com/Hxxxz0/RadioFlow}{GitHub}.
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